

'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_    						=       logging.get_logger(__name__)






def 				_lowerCAmelCase			(   __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]=False   , __magic_name__						:     List[Any]=False   , __magic_name__						:     List[str]=False						)				->  List[str]:
						lowercase :							Any    		=[]
						for i in range(config.num_hidden_layers						):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((f'''transformer.blocks.{i}.norm1.weight''', f'''vilt.encoder.layer.{i}.layernorm_before.weight''')						)
												rename_keys.append((f'''transformer.blocks.{i}.norm1.bias''', f'''vilt.encoder.layer.{i}.layernorm_before.bias''')						)
												rename_keys.append(
												    (f'''transformer.blocks.{i}.attn.proj.weight''', f'''vilt.encoder.layer.{i}.attention.output.dense.weight''')						)
												rename_keys.append(
												    (f'''transformer.blocks.{i}.attn.proj.bias''', f'''vilt.encoder.layer.{i}.attention.output.dense.bias''')						)
												rename_keys.append((f'''transformer.blocks.{i}.norm2.weight''', f'''vilt.encoder.layer.{i}.layernorm_after.weight''')						)
												rename_keys.append((f'''transformer.blocks.{i}.norm2.bias''', f'''vilt.encoder.layer.{i}.layernorm_after.bias''')						)
												rename_keys.append(
												    (f'''transformer.blocks.{i}.mlp.fc1.weight''', f'''vilt.encoder.layer.{i}.intermediate.dense.weight''')						)
												rename_keys.append((f'''transformer.blocks.{i}.mlp.fc1.bias''', f'''vilt.encoder.layer.{i}.intermediate.dense.bias''')						)
												rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.weight''', f'''vilt.encoder.layer.{i}.output.dense.weight''')						)
												rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.bias''', f'''vilt.encoder.layer.{i}.output.dense.bias''')						)

						# embeddings
						rename_keys.extend(
						    [
						        # text embeddings
						        ('''text_embeddings.word_embeddings.weight''', '''vilt.embeddings.text_embeddings.word_embeddings.weight'''),
						        (
						            '''text_embeddings.position_embeddings.weight''',
						            '''vilt.embeddings.text_embeddings.position_embeddings.weight''',
						        ),
						        ('''text_embeddings.position_ids''', '''vilt.embeddings.text_embeddings.position_ids'''),
						        (
						            '''text_embeddings.token_type_embeddings.weight''',
						            '''vilt.embeddings.text_embeddings.token_type_embeddings.weight''',
						        ),
						        ('''text_embeddings.LayerNorm.weight''', '''vilt.embeddings.text_embeddings.LayerNorm.weight'''),
						        ('''text_embeddings.LayerNorm.bias''', '''vilt.embeddings.text_embeddings.LayerNorm.bias'''),
						        # patch embeddings
						        ('''transformer.cls_token''', '''vilt.embeddings.cls_token'''),
						        ('''transformer.patch_embed.proj.weight''', '''vilt.embeddings.patch_embeddings.projection.weight'''),
						        ('''transformer.patch_embed.proj.bias''', '''vilt.embeddings.patch_embeddings.projection.bias'''),
						        ('''transformer.pos_embed''', '''vilt.embeddings.position_embeddings'''),
						        # token type embeddings
						        ('''token_type_embeddings.weight''', '''vilt.embeddings.token_type_embeddings.weight'''),
						    ]						)

						# final layernorm + pooler
						rename_keys.extend(
						    [
						        ('''transformer.norm.weight''', '''vilt.layernorm.weight'''),
						        ('''transformer.norm.bias''', '''vilt.layernorm.bias'''),
						        ('''pooler.dense.weight''', '''vilt.pooler.dense.weight'''),
						        ('''pooler.dense.bias''', '''vilt.pooler.dense.bias'''),
						    ]						)

						# classifier head(s)
						if vqa_model:
												# classification head
												rename_keys.extend(
												    [
												        ('''vqa_classifier.0.weight''', '''classifier.0.weight'''),
												        ('''vqa_classifier.0.bias''', '''classifier.0.bias'''),
												        ('''vqa_classifier.1.weight''', '''classifier.1.weight'''),
												        ('''vqa_classifier.1.bias''', '''classifier.1.bias'''),
												        ('''vqa_classifier.3.weight''', '''classifier.3.weight'''),
												        ('''vqa_classifier.3.bias''', '''classifier.3.bias'''),
												    ]						)
						elif nlvr_model:
												# classification head
												rename_keys.extend(
												    [
												        ('''nlvr2_classifier.0.weight''', '''classifier.0.weight'''),
												        ('''nlvr2_classifier.0.bias''', '''classifier.0.bias'''),
												        ('''nlvr2_classifier.1.weight''', '''classifier.1.weight'''),
												        ('''nlvr2_classifier.1.bias''', '''classifier.1.bias'''),
												        ('''nlvr2_classifier.3.weight''', '''classifier.3.weight'''),
												        ('''nlvr2_classifier.3.bias''', '''classifier.3.bias'''),
												    ]						)
						else:
												pass

						return rename_keys






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     Optional[int]						)				->  int:
						for i in range(config.num_hidden_layers						):
												lowercase :							Dict    		='''vilt.'''
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												lowercase :							Optional[int]    		=state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight'''						)
												lowercase :							Dict    		=state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.bias'''						)
												# next, add query, keys and values (in that order) to the state dict
												lowercase :							Dict    		=in_proj_weight[
												    : config.hidden_size, :
												]
												lowercase :							Any    		=in_proj_bias[: config.hidden_size]
												lowercase :							Any    		=in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowercase :							List[Any]    		=in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowercase :							List[str]    		=in_proj_weight[
												    -config.hidden_size :, :
												]
												lowercase :							Tuple    		=in_proj_bias[-config.hidden_size :]






def 				_lowerCAmelCase			(   __magic_name__						:     Tuple						)				->  Tuple:
						lowercase :							Optional[int]    		=['''head.weight''', '''head.bias''']
						for k in ignore_keys:
												state_dict.pop(__magic_name__   , __magic_name__						)






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Union[str, Any]						)				->  Union[str, Any]:
						lowercase :							Union[str, Any]    		=dct.pop(__magic_name__						)
						lowercase :							str    		=val






@torch.no_grad()
def 				_lowerCAmelCase			(   __magic_name__						:     Tuple   , __magic_name__						:     int						)				->  List[Any]:
						lowercase :							Tuple    		=ViltConfig(image_size=384   , patch_size=32   , tie_word_embeddings=__magic_name__						)
						lowercase :							Optional[Any]    		=False
						lowercase :							List[str]    		=False
						lowercase :							Tuple    		=False
						lowercase :							Dict    		=False
						if "vqa" in checkpoint_url:
												lowercase :							Optional[int]    		=True
												lowercase :							Optional[Any]    		=3129
												lowercase :							List[Any]    		='''huggingface/label-files'''
												lowercase :							str    		='''vqa2-id2label.json'''
												lowercase :							List[Any]    		=json.load(open(hf_hub_download(__magic_name__   , __magic_name__   , repo_type='''dataset'''						)   , '''r'''						)						)
												lowercase :							Optional[Any]    		={int(__magic_name__						): v for k, v in idalabel.items()}
												lowercase :							List[str]    		=idalabel
												lowercase :							Union[str, Any]    		={v: k for k, v in idalabel.items()}
												lowercase :							Dict    		=ViltForQuestionAnswering(__magic_name__						)
						elif "nlvr" in checkpoint_url:
												lowercase :							Optional[Any]    		=True
												lowercase :							List[str]    		=2
												lowercase :							str    		={0: '''False''', 1: '''True'''}
												lowercase :							str    		={v: k for k, v in config.idalabel.items()}
												lowercase :							Any    		=3
												lowercase :							Dict    		=ViltForImagesAndTextClassification(__magic_name__						)
						elif "irtr" in checkpoint_url:
												lowercase :							Dict    		=True
												lowercase :							Optional[Any]    		=ViltForImageAndTextRetrieval(__magic_name__						)
						elif "mlm_itm" in checkpoint_url:
												lowercase :							List[Any]    		=True
												lowercase :							List[str]    		=ViltForMaskedLM(__magic_name__						)
						else:
												raise ValueError('''Unknown model type'''						)

						# load state_dict of original model, remove and rename some keys
						lowercase :							Dict    		=torch.hub.load_state_dict_from_url(__magic_name__   , map_location='''cpu'''						)['''state_dict''']
						lowercase :							Any    		=create_rename_keys(__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__						)
						for src, dest in rename_keys:
												rename_key(__magic_name__   , __magic_name__   , __magic_name__						)
						read_in_q_k_v(__magic_name__   , __magic_name__						)
						if mlm_model or irtr_model:
												lowercase :							List[Any]    		=['''itm_score.fc.weight''', '''itm_score.fc.bias''']
												for k in ignore_keys:
																		state_dict.pop(__magic_name__   , __magic_name__						)

    # load state dict into HuggingFace model
						model.eval()
						if mlm_model:
												lowercase		,	lowercase :							Any    		=model.load_state_dict(__magic_name__   , strict=__magic_name__						)
												assert missing_keys == ["mlm_score.decoder.bias"]
						else:
												model.load_state_dict(__magic_name__						)

						# Define processor
						lowercase :							List[str]    		=ViltImageProcessor(size=384						)
						lowercase :							Any    		=BertTokenizer.from_pretrained('''bert-base-uncased'''						)
						lowercase :							Union[str, Any]    		=ViltProcessor(__magic_name__   , __magic_name__						)

						# Forward pass on example inputs (image + text)
						if nlvr_model:
												lowercase :							Any    		=Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'''   , stream=__magic_name__						).raw						)
												lowercase :							Dict    		=Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'''   , stream=__magic_name__						).raw						)
												lowercase :							str    		=(
												    '''The left image contains twice the number of dogs as the right image, and at least two dogs in total are'''
												    ''' standing.'''
												)
												lowercase :							List[Any]    		=processor(__magic_name__   , __magic_name__   , return_tensors='''pt'''						)
												lowercase :							Optional[int]    		=processor(__magic_name__   , __magic_name__   , return_tensors='''pt'''						)
												lowercase :							Union[str, Any]    		=model(
												    input_ids=encoding_a.input_ids   , pixel_values=encoding_a.pixel_values   , pixel_values_a=encoding_a.pixel_values   , )
						else:
												lowercase :							List[str]    		=Image.open(requests.get('''http://images.cocodataset.org/val2017/000000039769.jpg'''   , stream=__magic_name__						).raw						)
												if mlm_model:
																		lowercase :							int    		='''a bunch of [MASK] laying on a [MASK].'''
												else:
																		lowercase :							Optional[Any]    		='''How many cats are there?'''
												lowercase :							Optional[int]    		=processor(__magic_name__   , __magic_name__   , return_tensors='''pt'''						)
												lowercase :							Optional[int]    		=model(**__magic_name__						)

						# Verify outputs
						if mlm_model:
												lowercase :							Union[str, Any]    		=torch.Size([1, 11, 30522]						)
												lowercase :							Optional[int]    		=torch.tensor([-1_2.5_0_6_1, -1_2.5_1_2_3, -1_2.5_1_7_4]						)
												assert outputs.logits.shape == expected_shape
												assert torch.allclose(outputs.logits[0, 0, :3]   , __magic_name__   , atol=1E-4						)

												# verify masked token prediction equals "cats"
												lowercase :							Optional[Any]    		=outputs.logits[0, 4, :].argmax(-1						).item()
												assert tokenizer.decode([predicted_id]						) == "cats"
						elif vqa_model:
												lowercase :							Optional[Any]    		=torch.Size([1, 3129]						)
												lowercase :							List[str]    		=torch.tensor([-1_5.9_4_9_5, -1_8.1_4_7_2, -1_0.3_0_4_1]						)
												assert torch.allclose(outputs.logits[0, :3]   , __magic_name__   , atol=1E-4						)
												assert outputs.logits.shape == expected_shape
												assert torch.allclose(outputs.logits[0, 0, :3]   , __magic_name__   , atol=1E-4						)

												# verify vqa prediction equals "2"
												lowercase :							Any    		=outputs.logits.argmax(-1						).item()
												assert model.config.idalabel[predicted_idx] == "2"
						elif nlvr_model:
												lowercase :							List[str]    		=torch.Size([1, 2]						)
												lowercase :							Optional[Any]    		=torch.tensor([-2.8_7_2_1, 2.1_2_9_1]						)
												assert torch.allclose(outputs.logits[0, :3]   , __magic_name__   , atol=1E-4						)
												assert outputs.logits.shape == expected_shape

						Path(__magic_name__						).mkdir(exist_ok=__magic_name__						)
						print(f'''Saving model and processor to {pytorch_dump_folder_path}'''						)
						model.save_pretrained(__magic_name__						)
						processor.save_pretrained(__magic_name__						)


if __name__ == "__main__":
							UpperCamelCase_    						=       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--checkpoint_url""",
							    default="""https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt""",
							    type=str,
							    help="""URL of the checkpoint you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
							)

							UpperCamelCase_    						=       parser.parse_args()
							convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













"""simple docstring"""





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizerFast
    SCREAMING_SNAKE_CASE		:	int		=					True






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      lowerCamelCase_       	=		DistilBertTokenizer.from_pretrained('distilbert-base-uncased'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]




